New drug candidates are discovered by testing compounds against targets, a process termed screening. Traditionally, screening was a relatively slow process, with major pharmaceutical companies able to screen hundreds or a few thousands of compounds per week. This was acceptable, because the available compounds and biological targets were quite limited in number.
Recent advances in compound synthesis (e.g. combinatorial chemistry) and in the identification of biological targets (from genomics, proteomics and other disciplines) have led to a change in the nature of screening. There are many more compounds and the number of targets is also projected to grow rapidly. The extent of the growth can be appreciated if one considers that current drugs target about 450 of the estimated 50,000 potential gene products, each of which is a possible target. This is to say nothing of the targets that will be made available from the study of gene products (proteins). Therefore, the number of tests that could be done has become very large and will continue to grow. Pharmaceutical screening departments are implementing technologies which promise to increase the rate of testing. Their logic is that the more tests conducted per unit of time, the more often a new drug candidate will be discovered.
Screening at high rates is termed “high throughput screening” (HTS), and may be defined as the process of making thousands or many thousands of tests per day. HTS requires instruments and robotics optimized for high throughput, and systems for this purpose have been disclosed (e.g. US published patent application No. 2001/0028510 to Ramm et al.).
Most commonly, the instruments and robotics used for HTS do not accommodate tissues. Rather, they are applied to compounds and isolated targets. A compound of interest (referred to as the compound) is tested against a target (another compound, receptor molecule, protein or other), using label incorporation or some other property to reflect molecular interactions between the compound and its target. High throughput testing of compounds against targets is termed “primary screening.” Given that primary screening makes many thousands of tests per day, and that a proportion of those tests yields compounds worthy of further investigation (“hits”, usually less than 0.5% of the screen), hits generated by primary screening are accumulating at an unprecedented rate. These hits must be evaluated in post-primary screening stages, to characterize the efficacy, toxicity and specificity of the hit compounds. With these factors characterized, a small number of the best-qualified hits (“leads”) can be moved into very costly and time-consuming pre-clinical and clinical trials.
Unfortunately, post-primary testing is more complex and much slower than primary testing. It is not enough to simply detect molecular interactions between compounds and isolated target molecules. Rather, compounds must be tested for interaction with tissues. Therefore, the accumulation of hits is now a major bottleneck within the drug discovery pipeline and there is a need for post-primary tests which can verify leads at rates higher than possible in the past.
The bottleneck can be mitigated if post-primary tests are efficient in demonstrating interactions of compounds with biology. One promising path is to perform post-primary assays upon cells. Cells can provide a more biologically relevant test than is obtained from a simple compound mixture. At the same time, cell assays are less costly, much quicker to conduct and more socially acceptable than assays conducted in complex organisms (e.g. rodents). It is projected that the importance of cell-based assays will continue to grow, as cellular models for ogranismic response continue to develop and improve.
A potential problem with cell assays is the relatively low level of throughput that most evidence. For example, a “metabolic rate” method is disclosed by Dawes (1972), and a “pooled quantity” method described in Freshney (1987). These types of low throughput techniques are typical of those used to analyze cell populations without the use of imaging or other high throughput methods of detection.
To achieve higher rates of throughput, image-based measurements may be made upon cell populations (e.g. Malay et al., 1989; Schroeder and Neagle, 1996; Ramm, 1999), and may be combined with various methods for automating and optimizing the processes of handling, imaging, and analyzing the cellular samples. In these disclosures, the entity of measurement is a population of cells within each of a plurality of wells in a microwell plate. Cellular or subcellular detail is not resolved.
Detection of cell population responses may be contrasted with a requirement for detection of effects occurring within discrete cells in a population. In this case, cellular or subcellular resolution is required and a number of systems and methods for microscopic cell screening have been developed. As with population screens, the key is to construct systems and methods which automate and optimize the processes of handling, imaging, and analyzing the cellular samples. With the present invention, automated cell screens can be conducted with single cell and subcellular resolution.
Image Cytometry
“Cytometry” is the measurement of features from discrete cells. “Image cytometry” is the use of imaging systems to perform cytometric measurements. Cytometric measurements may or may not require subcellular detail. If discrete cells are imaged at low resolution, each cell occupies a small number of image pixels and is treated as a homogenous measurement point (e.g. Miraglia et al., 1999). We refer to these as “point cell assays.” Cellular anatomy can also be resolved at higher resolution, with parts of cells each occupying numbers of pixels. The level of subcellular resolution ranges from the visualization of only the largest structures (e.g. Galbraith et al., 1991), to the resolving of subcellular organelles (most of the material dealt with in this body of art). Common classes of cytometric measurement include:
Morphometry—the size, shape, and texture of cells, nuclei and organelles. For example:                Neurite outgrowth is used as an index of neural development or regeneration (Masseroli et al., 1993; Siklos et al, 1993; Malgrange et al, 1994; Mezin et al, 1994; Turner et al, 1994; de Medinaceli et al, 1995; Pauwels et al, 1995; Ventimiglia et al, 1995; Stahlhut et al, 1997; Isaacs et al, 1998; Bitsland et al, 1999; Pollack et al, 1999; Ronn et al, 2000).        Changes in nuclear size, shape and chromatin distribution can be correlated with progression through the cell cycle. (e.g. De Le Torre and Navarrete, 1974; Sawicki, et al., 1974; Giroud, 1982), or with classification of proliferative tendencies (e.g. Crissman et al., 1990; Martin et al., 1984; Smith et al., 1989; Souchier et al., 1995).        
Morphometry is commonly implemented upon diagnostic imaging cytometers. These are automated devices, which incorporate dedicated components and software methods for clinical screening (e.g. as disclosed in Lee et al., 1992; Wied et al., 1987; U.S. Pat. Nos. 5,281,517; 5,287,272; 5,627,908; 5,741,648; 5,978,498; 6,271,036; 6,252,979).
Functional analysis—It is common to measure the amount of a substance or comparative amounts of a substance or substances within subcellular compartments, and to use that measurement as an index of cellular function.                Ion channels Changes in cellular electrical potential reflect the operation of ion channels. Intracellular label localization can be used as an alternative to electrophysiology, to investigate the operation of ion channels (e.g. review in Taylor et al., 2001; Omalley, 1994).).        Translocation (movement of proteins between subcellular compartments) Proteins are localized in two types of subcellular compartments. They may be embedded in or associated with membranes (e.g. receptors decorating a cell membrane), or they may be in an aqueous phase (in nucleoplasm or cytoplasm). Many cellular functions are associated with protein transitions between these compartments. Functional imaging can be used to examine localization to specific intracellular receptor compartments (e.g. Luby-Phelps et al., 1985) or trafficking of receptors between cellular compartments. For example, Georget et al. (1998) and Trapman and Brinkmann (1993) disclose the analysis of receptor localization using imaging quantification of the nuclear/cytoplasmic ratio. A fluor labels the receptor, and movement of the fluor reflects alteration in the location of receptor molecules between nucleus and cytoplasm.        Localization (amount of protein within a cellular or subcellular compartment) Abundance of any (e.g. structural) proteins in subcellular compartments (e.g. nucleus and cytoplasm) can be used as an index of function (e.g. of proliferative tendency as in Kawamoto et al., 1997).        
Cytometric systems for morphometry and functional analysis may be built around image analyzers of the type marketed by many commercial entities. Some such systems are designed for application in research labs (research systems), and require frequent operator interaction to perform their function. Therefore, these systems investigate a small number of specimens in a given time period. An example of such a system is the MCID image analyzer from Imaging Research Inc. Other such systems are designed for application in industrial drug discovery (industrial systems) or cell diagnostics (diagnostic systems), and they function without frequent operator interaction (automated), and investigate a relatively large number of specimens in a given period (termed “high throughput”). Examples of industrial high throughput systems are the AutoLead Cell Analyzer from Imaging Research Inc. and the ArrayScan II from Cellomics Inc. An example of a cell diagnostic system is the LSC from CompuCyte Inc.
Numerous publications generated with research systems describe methods for making morphometric and functional measurements upon cells. Widely known examples of such measurements include ratios of size or label intensity between nucleus and cytoplasm, or the relative intensity of fluorescence (as generated by standard fluorescence methods or spatially dependent methods such as fluorescence resonance energy transfer), emitted at multiple wavelengths.
Research systems have a theoretical application to diagnosis and screening, in that they can be programmed and operated to implement any cell detection method (e.g. Serra, 1982 is often cited). Most industrial and diagnostic systems use known image processing methods which have also been implemented on research systems to enhance the detection of cells in images.
However, research systems lack the automation and throughput which would make them useful for industrial drug discovery or clinical diagnosis. Most commonly, an operator must interact with the system on a frequent basis. For example, Bacus (U.S. Pat. No. 5,018,209) discloses one such operator-assisted diagnostic system, which is useful with small numbers of samples, but which would not be useful in a high throughput environment.
Methods Employed in Cytometric Imaging Systems
Presegmentation
It is common to preprocess images to enhance the detectability of features. For example, certain convolution filters such as the Prewitt (O'Gorman et al., 1985) and Hueckel (Hueckel, 1971) can sometimes better demonstrate a cell periphery than unfiltered images. Such methods improve the accuracy of subsequent segmentation and can result in a reduced requirement for operator editing of segmented pixels.
Other widely known corrections are applied to correct inhomegenities within the collection optics and illumination field, and to correct local (e.g. as disclosed in U.S. Pat. No. 5,072,382) or global (as commonly applied in many commercial imaging systems) background variations. In this respect, it is common to acquire an image of a blank field, process the image in some way to remove high frequency intensity variations, calculate a deviation from a reference pixel value at each location in the processed image, and save the matrix of deviation factors as a correction matrix (e.g. as reduced to practice in the MCID system from Imaging Research). The correction matrix is used to improve the homogeneity of the background in subsequent images.
Segmentation
Before a measurement may be made, relevant image features must be discriminated from background. This discrimination is performed using widely known methods for image segmentation (reduced to practice in many commercial products, e.g. the ImagePro software from Media Cybernetics). Segmentation is defined as the process that subdivides an image into its constituent parts or objects. Tracing and thresholding are known methods for segmentation (there are others). Ideally, a simple staining process yields unambiguous detection of cells or cellular components, wherein each stained object marks a feature of interest, and other image components are unstained. The goal is that the objects are bright or dark enough to be detected with a simple intensity criterion. In practice, this goal is rarely achieved.
Tracing
The simplest manual segmentation method is for the human operator to trace cells and subcellular detail. The system then uses pixels within the trace to report parameters of interest (e.g. Deligdisch et al., 1993; Gil et al., 1986).
Thresholding
The simplest automated segmentation method, intensity thresholding, takes a grayscale or color image as input, histograms the intensity frequencies, and outputs a binary image based on a single discriminating value (the threshold). Simple intensity or color thresholding is rarely adequate for industrial applications in that only some of the segmented pixels are valid and the segmented image needs operator editing. For example, Takamatsu et al. (1986) report that simple intensity thresholding resulted in lower precision for cell detection than was attained by flow cytometry. There are many problems, including cell and background intensities that vary from location to location in a single image or set of images.
Target Regions
Once image pixels are segmented as being of possible relevance, they must be classified as fitting within features of interest (termed regions or targets). The point is to group pixels to distinct regions according to criteria of homogeneity. Homogeneity criteria are based on some parameter (e.g. distance separating detected pixels), which can be derived in a variety of known ways. Among techniques for region extraction, the least complex method involves manual or semi-automated extraction. In this process people confirm or identify the assignment of segmented pixels to regions.
“Region growing” is the process of amalgamating separated segmented pixels into regions. There are many criteria that can be used for region growing (e.g. Chassery and Garbay, 1984; Garbay 1986; Ong et al., 1993; Smeulders et al. 1979). For example, geometric features (e.g. distance from another region, size, shape, texture, frequency distribution, fractal dimensions, local curvature) or statistical features (e.g. variance, mode, skewness, kurtosis, entropy) could be used as part of the classification of pixels to regions. Region growing can also be based on morphological techniques. For example, Seniuk et al., 1991 and U.S. Pat. No. 5,978,498 disclose the use of morphology in a series of steps using intensity-based masks to discriminate nuclear and cytoplasmic compartments, followed by erosion (to extract a clean nucleus) and dilation (to extract a clean cytoplasmic area).
Grown regions can then be passed to various higher level processes. For example, complex pixel statistics (e.g. multiscale wavelet maxima as disclosed in U.S. Pat. No. 6,307,957) can be applied to make measurements upon regions. Similarly, knowledge based methods for cellular classification take regions as input and make decisions as their output. These systems can incorporate expert systems and/or neural nets (e.g. U.S. Pat. No. 5,287,272; Refenes et al., 1990; Stotzka et al., 1995).
Cell Screening Systems
Research systems which use assemblages of known methods for measuring probe level within cells are widely disclosed (e.g. Macaulay and Palcic, 1990; Mize et al., 1988; Thompson et al., 1990; Zoli et al. 1990). Similarly, industrial cell screening systems implement known methods for presegmentation, segmentation, and target classification (e.g. as in the ArrayScan system from Cellomics and the InCell system from Amersham Biosciences). What distinguishes research and industrial systems from each other is that the industrial system will function with minimal operator interaction (automatically) and will provide higher rates of throughput. Research applications can be accomplished on almost any image analysis system. Automation and throughput can only be achieved within a system integrating specialized software and hardware.
As an example, a widely applied principle is that of marking a readily detected subcellular component, in order to improve subsequent detection of cell locations and of subcellular components adjacent to the marked component. Commonly, the marked component is a nucleus (e.g. as disclosed in Benveniste et al., 1989; Lockett et al., 1991; Anderson et al., 1992; Santisteban et al., 1992). In an industrial application (e.g. as disclosed in U.S. Pat. No. 5,989,835 and as supplied with the ArrayScan II from Cellomics, Inc.), cytoplasm around a marked nucleus can be defined (automatically) by an annulus so as to minimize intrusion of one cell cytoplasm upon another (the cytoplasm of which lies beyond the annulus). The same annulus method can be implemented on a research system, but without automation of the microscope system and software so as to operate with minimal user interaction and high throughput. Specifically, Seniuk et al. (1991) disclose a method for marking cell nuclei with a DNA-specific fluorescent probe, and then creating an annulus at a distance from the nucleus (in this case, 1 μm distance was used) for image-based measurements of cytoplasmic probe content.
Marking of cellular components and use of these components to localize other components are known methods. However, the assemblage of known methods into systems and methods usable in industrial cell screening systems constitutes novelty to the extent that these systems and methods yield better automation and throughput than is available in the prior art. The difficulty of creating such an automated and high throughput system is not to be underestimated, and is demonstrated by the very small number of such systems which have been disclosed or reduced to practice (e.g. Proffit et al., 1996; Ramm et al., 2001, 2002; U.S. Pat. No. 5,989,835; U.S. Pat. No. 6,103,479).
The present invention provides a system and process which achieve improvements in the following areas:                Presegmentation and segmentation Known methods for image processing are implemented in such a way that automated segmentation is achieved (e.g. as disclosed in Ramm et al., published U.S. patent application 2001/0028510).        Measurement Sets of known measurements (pixel counting, etc.) are implemented as methods which demonstrate aspects of biology in a reliable fashion (e.g. as disclosed in Ramm et al., 2001/0028510).        Optics, mechanicals and electronics Components for automated positioning, focusing, imaging and processing of a multiplicity of samples are integrated as systems within which the segmentation and measurement methods may be mounted.COMPONENTS and methods are adapted into systems which yield more highly automated and more rapid cell screening.        
In accordance with one aspect of the invention a library is provided of assay processing procedures that are structured into methods that perform automated analyses with minimal user interaction. Members of the library are:                Nonlinear suppression of high intensity peaks        Adaptive noise smoothing (Gaussian)        Adaptive noise smoothing and feature enhancement by nonlinear diffusion filtering        Thresholding by optimal histogram bipartition        Seeded region growing        Texture transform        Morphological refinement of detected features        Quantification by local contrast        Distributional feature analyses        Frequency domain detection of granular details        Demarcation mapping        Background correction        SievingDisclosed methods include neurite assays, granular translocation assays, nuclear translocation assays, and membrane ruffling assays.        
In accordance with another aspect of the present invention, the methods are integrated within an automated opto-mechanical system that positions specimens located in a plurality of containers, focuses, and interfaces to laboratory automation equipment.
In accordance with a further aspect, the invention includes an electronic camera and computer, used to acquire and store images, and to host the software.